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Abstract Background Tunnel placement is a key step in anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the accuracy of bone tunnel drilling in arthroscopic ACL reconstruction assisted by a three-dimensional (3D) image-based robot system. Methods Robot-assisted ACL reconstruction was performed on twelve freshly frozen knee specimens. During the operation, three-dimensional images were used for ACL bone tunnel planning, and the robotic arm was used for navigation and drilling. Twelve patients who underwent traditional arthroscopic ACL reconstruction were included. 3D computed tomography was used to measure the actual position of the ACL bone tunnel and to evaluate the accuracy of the robotic and traditional ACL bone tunnel. Results On the femoral side, the positions of robotic and traditional surgery tunnels were 29.3 ± 1.4% and 32.1 ± 3.9% in the deep-to-shallow direction of the lateral femoral condyle (p = 0.032), and 34.6 ± 1.2% and 21.2 ± 9.4% in the high-to-low direction (p

This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization of both primary and coupled thoracic spine motions. To validate the system, this study employed an Inter-days experimental setting to conduct experiments involving a total of 957 thoracic spine movements, with participation from two representatives of varying age and gender. The reliability of the proposed system was assessed using the Intraclass Correlation Coefficient (ICC) and Standard Error of Measurement (SEM). The experimental results demonstrated strong ICC values for various thoracic spine movements across different planes, ranging from 0.774 to 0.918, with an average of 0.85. The SEM values ranged from 0.64° to 4.03°, with an average of 1.93°. Additionally, we successfully conducted an assessment of thoracic spine mobility in a stroke rehabilitation patient using the system. This illustrates the feasibility of the system for actively analyzing thoracic spine mobility, offering an effective technological means for non-invasive research on thoracic spine activity during continuous movement states.

The next generation of all-optical computation platforms prefers the light-guiding-light (LGL) scheme inside a medium that envisions circuitry-free and rapidly reconfigurable systems powered by dynamic interactions between light beams. Currently, suitable LGL materials and corresponding mechanisms are in urgent need. In this work, we proposed ubiquitous air as a restorable LGL signal manipulation medium with transient air-plasma waveguide circuits. Briefly, by focusing femtosecond laser beams in free space, the created atmospheric plasma filament array via photoionization was able to guide terahertz (THz) pulses along its epsilon-near-zero zone with a 1/f-profile spectral response. Consequently, this achieved a time-domain integration of the THz pulse in broad bandwidth. When the pumping laser was sequentially turned off and on, this air-plasma multi-filament structure was erased and rebuilt within nano- and femto-seconds, respectively, allowing rapid and repeated rearrangements of the all-optical stage. Furthermore, this air-based LGL information processing approach is promising to pave the way toward all-optical calculations during free-space directional transmission of THz waves, in which way the delivered THz signal can be remotely controlled.

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method.

Human wrist motion decoding with a biological-signal-based interface is a key technique in the upper-limb exoskeleton and prosthesis control. One critical issue in this field is achieving high recognition precision and fast time response while against external disturbances of sensor re-wearing. In this study, we proposed a high-framerate Electrical Impedance Tomography (EIT) system combined with an adaptive recognition algorithm for real-time wrist kinematics decoding. The high-framerate EIT system was developed by a parallel stimulation-measurement sequence, and the sampling rate was as high as 104 Hz. Compared to the most widely used myoelectric techniques, the EIT-based interface can provide extra deep muscular spatial information with similar surface electrodes. It greatly benefited the subsequent recognition algorithms, in which the key EIT regions indicating muscle morphology kept consistent after an arbitrary sensor re-donning. The designed adaptive algorithm achieved equally high performance with an automatic update of the classifier mean values with a fast self-operated calibration process. We validated the approach on 12 subjects with a 2-dimensional Fitts’ law test. The wrist gestures and joint angles were mapped to the direction and speed of the cursor movement, respectively. The average throughputs (TPs) of Fitts’ law tests were 1.0269 ± 0.0971 bits/s and 1.0095 ± 0.0931 bits/s without and with sensor re-donning, respectively, which were comparable to the TPs of sEMG-based studies. The results showed the promise of the EIT-based interface on real-time human motion intent recognition. Future endeavors are worth being paid in this direction for more complicated robotic tasks.

During Fischer–Tropsch synthesis, O atoms are dissociated on the surface of Fe-based catalysts. However, most of the dissociated O would be removed as H2O or CO2, which results in a low atom economy. Hence, a comprehensive study of the O removal pathway as formic acid has been investigated using the combination of density functional theory (DFT) and kinetic Monte Carlo (kMC) to improve the economics of Fischer–Tropsch synthesis on Fe-based catalysts. The results show that the optimal pathway for the removal of dissociated O as formic acid is the OH pathway, of which the effective barrier energy (0.936 eV) is close to that of the CO activation pathway (0.730 eV), meaning that the removal of dissociated O as formic acid is possible. The main factor in an inability to form formic acid is the competition between the formic acid formation pathway and other oxygenated compound formation pathways (H2O, CO2, methanol-formaldehyde); the details are as follows: 1. If the CO is hydrogenated first, then the subsequent reaction would be impossible due to its high effective Gibbs barrier energy. 2. If CO reacts first with O to become CO2, it is difficult for it to be hydrogenated further to become HCOOH because of the low adsorption energy of CO2. 3. When the CO + OH pathway is considered, OH would react easily with H atoms to form H2O due to the hydrogen coverage effect. Finally, the removal of dissociated O to formic acid is proposed via improving the catalyst to increase the CO2 adsorption energy or CO coverage.

Trajectory planning of the knee joint plays an essential role in controlling the lower limb prosthesis. Nowadays, the idea of mapping the trajectory of the healthy limb to the motion trajectory of the prosthetic joint has begun to emerge. However, establishing a simple and intuitive coordination mapping is still challenging. This paper employs the method of experimental data mining to explore such a coordination mapping. The coordination indexes, i.e., the mean absolute relative phase (MARP) and the deviation phase (DP), are obtained from experimental data. Statistical results covering different subjects indicate that the hip motion possesses a stable phase difference with the knee, inspiring us to construct a hip-knee Motion-Lagged Coordination Mapping (MLCM). The MLCM first introduces a time lag to the hip motion to avoid conventional integral or differential calculations. The model in polynomials, which is proved more efficient than Gaussian process regression and neural network learning, is then constructed to represent the mapping from the lagged hip motion to the knee motion. In addition, a strong linear correlation between hip-knee MARP and hip-knee motion lag is discovered for the first time. By using the MLCM, one can generate the knee trajectory for the prosthesis control only via the hip motion of the healthy limb, indicating less sensing and better robustness. Numerical simulations show that the prosthesis can achieve normal gaits at different walking speeds.

Recognition of continuous foot motions is important in robot-assisted lower limb rehabilitation, especially in prosthesis and exoskeleton design. For instance, perceiving foot motion is essential feedback for the robot controller. However, few studies have focused on perceiving multiple-degree of freedom (DOF) foot movements. This paper proposes a novel human-machine interaction (HMI) recognition wearable system for continuous multiple-DOF ankle-foot movements. The proposed system uses solely kinematic signals from inertial measurement units and multiclass support vector machines by creating error-correcting output codes. We conducted a study with multiple participants to validate the performance of the system using two strategies, a general model and a subject-specific model. The experimental results demonstrated satisfactory performance. The subject-specific approach achieved 98.45% ± 1.17% (mean ± SD) overall accuracy within a prediction time of 10.9 ms ± 1.7 ms, and the general approach achieved 85.3% ± 7.89% overall accuracy within a prediction time of 14.1 ms ± 4.5 ms. The results prove that the proposed system can more effectively recognize multiple continuous DOF foot movements than existing strategies. It can be applied to ankle-foot rehabilitation and fills the HMI high-level control demand for multiple-DOF wearable lower-limb robotics.

Human-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing array, was used for inter-day hand gesture recognition. The performance of the entire system was further improved by utilizing a triplet network for deep feature embedding. Seven hand gestures were included into the gesture set, and inter-day experiments which lasted for five consecutive days with three sessions on each day were conducted. Five healthy subjects participated in the experiment. Between each session, the wristband was doffed, and re-donned before the next session. The triplet network achieved an average recognition accuracy of 91.98% across all the sessions of all the subjects, and yielded a higher classification result (p < 0.05) over the convolutional neural network trained with softmax-cross-entropy loss (with an average accuracy of 84.65%). Furthermore, we also found that the capacitive array size had an evident influence on the inter-day classification result. The array with the full size (thirty-two channels) achieved a higher average recognition accuracy over all the down-sampled arrays. This work demonstrated the feasibility of improving the hand gesture recognition performance over days of usage by fabricating a wearable, flexible multi-channel capacitive wristband and implementing the triplet network.

Abstract Background For people with lower-limb amputations, wearing a prosthetic limb helps restore their motor abilities for daily activities. However, the prosthesis's potential benefits are hindered by limited somatosensory feedback from the affected limb and its prosthesis. Previous studies have examined various sensory substitution systems to alleviate this problem; the prominent approach is to convert foot–ground interaction to tactile stimulations. However, positive outcomes for improving their postural stability are still rare. We hypothesized that the sensory substiution system based on surrogated tactile stimulus is capable of improving the standing stability among people with lower-limb amputations. Methods We designed a wearable device consisting of four pressure sensors and two vibrators and tested it among people with unilateral transtibial amputations (n = 7) and non-disabled participants (n = 8). The real-time measurements of foot pressure were fused into a single representation of foot–ground interaction force, which was encoded by varying vibration intensity of the two vibrators attached to the participants’ forearm. The vibration intensity followed a logarithmic function of the force representation, in keeping with principles of tactile psychophysics. The participants were tested with a classical postural stability task in which visual disturbances perturbed their quiet standing. Results With a brief familiarization of the system, the participants exhibited better postural stability against visual disturbances when switching on sensory substitution than without. The body sway was substantially reduced, as shown in head movements and excursions of the center of pressure. The improvement was present for both groups of participants and was particularly pronounced in more challenging conditions with larger visual disturbances. Conclusions Substituting otherwise missing foot pressure feedback with vibrotactile signals can improve postural stability for people with lower-limb amputations. The design of the mapping between the foot–ground interaction force and the tactile signals is essential for the user to utilize the surrogated tactile signals for postural control, especially for situations that their postural control is challenged.

Osteoporotic fractures, also known as fragility fractures, are prevalent in the elderly and bring tremendous social burdens. Poor bone quality, weak repair capacity, instability, and high failure rate of internal fixation are main characteristics of osteoporotic fractures. Osteoporotic bone defects are common and need to be repaired by appropriate materials. Proximal humerus, distal radius, tibia plateau, calcaneus, and spine are common osteoporotic fractures with bone defect. Here, the consensus from the Osteoporosis Group of Chinese Orthopaedic Association concentrates on the epidemiology, characters, and management strategies of common osteoporotic fractures with bone defect to standardize clinical practice in bone repair of osteoporotic fractures.

Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET.To improve the sampling efficiency, the sampling interval was calculated based on linear probability.The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training.With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment.AUC, MAP, and Error Rate were employed as evaluation indexes.The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy.

Continuous gait phase plays an important role in robotic prosthesis control. In this paper, we have conducted the offline adaptive estimation (at different speeds and on different ramps) of continuous gait phase of robotic transtibial prosthesis based on the adaptive oscillators. We have used the capacitive sensing method to record the deformation of the muscles. Two transtibial amputees joined in this study. Based on the strain signals of the prosthetic foot and the capacitive signals of the residual limb, the maximum and minimum of estimation errors are 0.80 rad and 0.054 rad, respectively, and their corresponding ratios in one gait cycle are 1.27% and 0.86%, respectively. This paper proposes an effective method to estimate the continuous gait phase based on the capacitive signals of the residual muscles, which provides a basis for the continuous control of robotic transtibial prosthesis.

This study proposed a multiple degree-of-freedom (DoF) continuous wrist angle estimation approach based on an electrical impedance tomography (EIT) interface. The interface can inspect the spatial information of deep muscles with a soft elastic fabric sensing band, extending the measurement scope of the existing muscle-signal-based sensors. The designed estimation algorithm first extracted the mutual correlation of the EIT regions with a kernel function, and second used a regularization procedure to select the optimal coefficients. We evaluated the method with different features and regression models on 12 healthy subjects when they performed six basic wrist joint motions. The average root-mean-square error of the 3-DoF estimation task was 7.62°, and the average R2 was 0.92. The results are comparable to state-of-the-art with sEMG signals in multi-DoF tasks. Future endeavors will be paid in this new direction to get more promising results.

Monitoring sleep conditions is of importance for sleep quality evaluation and sleep disease diagnosis. Accurate respiration detection provides key information about sleep conditions. Here, we propose a perforated temperature sensor that can be worn below the nasal cavity to monitor breath. The sensing system consists of two perforated temperature sensors, signal conditioning circuits, a transmission module, and a supporting analysis algorithm. The perforated structure effectively enhances the sensitivity of the system and shortens the response time. The sensor’s response time is 0.07 s in air and sensitivity is 1.4‰°C−1. The device can achieve a monitoring respiratory temperature range between normal room temperature and 40 °C. The simple and standard micromachining process ensures low cost and high reproducibility. We achieved the monitoring of different breathing patterns, such as normal breathing, panting, and apnea, which can be applied to sleep breath monitoring and exercise information recording.

Aiming at the difficult about lost motion calculation of micro-segment planetary gear transmission accuracy. Considering the influence of gear natural error， assembly error and the time-varying mesh stiffness of gears， the lost motion model of planetary gear transmission is proposed based on the backlash of micro-segment gear. Aiming at 3K-I planetary gear train， the theoretical calculation lost motion of micro-segment gear is 5.92% smaller than involute gear by Monte Carlo simulation. Experiment under the same conditions shows that the lost motion of micro-segment planetary gear is within the theoretical model calculation and the lost motion of micro-segment planetary gear is 6.25% smaller than involute gear， which proves that the lost motion model of micro-segment planetary gear is correct.

The lower-limb robotic prostheses can provide assistance for amputees’ daily activities by restoring the biomechanical functions of missing limb(s). To set proper control strategies and develop the corresponding controller for robotic prosthesis, a prosthesis user’s intent must be acquired in time, which is still a major challenge and has attracted intensive attentions. This work focuses on the robotic prosthesis user’s locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective (locomotion mode recognition, gait event detection, and continuous gait phase estimation) and reviews the state-of-the-art intent recognition techniques in a lower-limb prosthesis scope. The current research status, including recognition approach, progress, challenges, and future prospects in the human’s intent recognition, has been reviewed. In particular for the recognition approach, the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition. This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.

Oxy-fuel combustion technology is an effective way to reduce CO2 emissions. An ionic liquid [emim][Tf2N] was used to capture the CO2 in flue gas from oxy-fuel combustion plant. The process of the CO2 capture was simulated using Aspen Plus. The results show that when the liquid–gas ratio is 1.55, the volume fraction of CO2 in the exhaust gas is controlled to about 2%. When the desorption pressure is 0.01 MPa, desorption efficiency is 98.2%. Additionally, based on the designability of ionic liquids, a hypothesis on the physical properties of ionic liquids is proposed to evaluate their influence on the absorption process and heat exchanger design. The process evaluation results show that an ionic liquid having a large density, a large thermal conductivity, and a high heat capacity at constant pressure is advantageous. This paper shows that from capture energy consumption and lean circulation, oxy-fuel combustion is a more economical method. Furthermore, it provides a feasible path for the treatment of CO2 in the waste gas of oxy-fuel combustion. Meanwhile, Aspen simulation helps speed up the application of ionic liquids and oxy-fuel combustion. Process evaluation helps in equipment design and selection.

The paper puts forward an on-board strategy for a training model and develops a real-time human locomotion mode recognition study based on a trained model utilizing two inertial measurement units (IMUs) of robotic transtibial prosthesis. Three transtibial amputees were recruited as subjects in this study to finish five locomotion modes (level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending) with robotic prostheses. An interaction interface was designed to collect sensors' data and instruct to train model and recognition. In this study, the analysis of variance ratio (no more than 0.05) reflects the good repeatability of gait. The on-board training time for SVM (Support Vector Machines), QDA (Quadratic Discriminant Analysis), and LDA (Linear discriminant analysis) are 89, 25, and 10 s based on a 10,000 × 80 training data set, respectively. It costs about 13.4, 5.36, and 0.067 ms for SVM, QDA, and LDA for each recognition process. Taking the recognition accuracy of some previous studies and time consumption into consideration, we choose QDA for real-time recognition study. The real-time recognition accuracies are 97.19 ± 0.36% based on QDA, and we can achieve more than 95% recognition accuracy for each locomotion mode. The receiver operating characteristic also shows the good quality of QDA classifiers. This study provides a preliminary interaction design for human–machine prosthetics in future clinical application. This study just adopts two IMUs not multi-type sensors fusion to improve the integration and wearing convenience, and it maintains comparable recognition accuracy with multi-type sensors fusion at the same time.

The separation of C6 alkane isomers is crucial to the petroleum refining industry, but the distillation methods in place are energy intensive. Here, the authors design a series of topologically-guided zirconium-based metal-organic frameworks with optimized pore structures for efficient C6 alkane isomer separations.

Cu-Mg-Al layered double hydroxides (LDHs) with amine modification were prepared by an organic combination of an anionic surfactant-mediated method and an ultrasonic spalling method using N-aminoethyl-γ-aminopropyltrimethoxysilane as a grafting agent. The materials were characterized by elemental analysis, XRD, SEM, FTIR, TGA, and XPS. The effects of the Cu2+ content on the surface morphology and the CO2 adsorption of Cu-Mg-Al LDHs were investigated, and the kinetics of the CO2 adsorption and the photocatalytic reduction of CO2 were further analyzed. The results indicated that the amine-modified method and appropriate Cu2+ contents can improve the surface morphology, the increase amine loading and the free-amino functional groups of the materials, which were beneficial to CO2 capture and adsorption. The CO2 adsorption capacity of Cu-Mg-Al N was 1.82 mmol·g−1 at 30 °C and a 0.1 MPa pure CO2 atmosphere. The kinetic model confirmed that CO2 adsorption was governed by both the physical and chemical adsorption, which could be enhanced with the increase of the Cu2+ content. The chemical adsorption was suppressed, when the Cu2+ content was too high. Cu-Mg-Al N can photocatalytically reduce CO2 to methanol with Cu2+ as an active site, which can significantly improve the CO2 adsorption and photocatalytic conversion.

1-allyloxy-3-(4-nonylphenoxy)-2-propanol polyoxyethylene ethers (ANAPEs), a new type of absorbent, are polymeric surfactants with different adduct numbers. In this work, ANAPEs, including SN-10 with adduct number of 10 and SN-15 with adduct number of 15, were prepared for CO2 absorption using the isochoric saturation method. Densities of the ANAPEs at atmospheric pressure were measured by a 5.567 ± 0.004 cm3 pycnometer, which decreased with increased temperature. Solubility data of CO2 in ANAPEs were measured within the pressure range of 0 – 600.0 kPa and temperature range of 303.15 – 323.15 K at 10 K intervals and could be calculated on the basis of experimental data of p, xCO2 and bCO2. The solubility of CO2 in absorbents increased linearly with increasing pressure and decreased with increasing temperature at all the pressures. The solubility of CO2 in SN-15 is the highest at all temperatures, but almost the same with SN-10 at 303.15 K over pressures (p

This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness. Nine motion patterns combined with 16 motion transitions are investigated on seven participants. We also designed an automatic data labeling method based on inertial signals from the measured hand, which greatly accelerated the training procedure. With the capacitive sensing system and the designed recognition algorithm, the method produced an average recognition of over 92%. Correct decisions could be made with approximately a 347-ms delay from the relaxed state to the time point of motion initiation. The confounding factors that affect the performances are also analyzed, including the sliding window length, the motion types and the external disturbances. We found the average accuracy increased to 98.7% when five motion patterns were recognized. The results of the study proved the feasibility and revealed the problems of the noncontact capacitive sensing approach on upper-limb motion sensing and recognition. Future efforts in this direction could be worthwhile for achieving more promising outcomes.

Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.

In this paper, we present an approach to sense human body capacitance and apply it to recognize lower limb locomotion modes. The proposed wearable sensing system includes sensing bands, a signal processing circuit and a gait event detection module. Experiments on long-term working stability, adaptability to disturbance and locomotion mode recognition are carried out to validate the effectiveness of the proposed approach. Twelve able-bodied subjects are recruited, and eleven normal gait modes are investigated. With an event-dependent linear discriminant analysis classifier and feature selection procedure, four time-domain features are used for pattern recognition and satisfactory recognition accuracies (97:3% ± 0:5%, 97:0% ± 0:4%, 95:6% ± 0:9% and 97:0% ± 0:4% for four phases of one gait cycle respectively) are obtained. The accuracies are comparable with that from electromyography-based systems and inertial-based systems. The results validate the effectiveness of the proposed lower limb capacitive sensing approach in recognizing human normal gaits.

In this paper, we aim to investigate the effect of Proprioceptive Neuromuscular Facilitation (PNF) based rehabilitation for ankle plantar flexors spasticity by using a Robotic Ankle-foot Rehabilitation System (RARS). A modified robot-assisted system was proposed and seven post-stroke patients with hemiplegic spastic ankles participated a three-month of robotic PNF training. Their impaired sides were used as the experimental group while their unimpaired sides as the control group. A robotic intervention for the experimental group generally started from a two minutes passive stretching to warm-up or relax the soleus and gastrocnemius muscle and also ended with the same one. Then a PNF training session included 30 trails was activated between them. The rehabilitation trainings were carried out three times a week as an addition of their regular rehabilitation exercise. Passive ankle joint range of motion, resistance torque and stiffness were measured in both ankles before and after the intervention. The changes in Achilles' tendon length, walking speed, and lower limb function were also evaluated by the same physician or physiotherapist for each participant. Biomechanical measurements before interventions showed significant difference between the experimental group and the control group due to ankle spasticity. For the control group, there was no significant difference in the three months with no robotic intervention. But for the experimental group, passive dorsiflexion range of motion increased ( p0.05 ). The robotic rehabilitation also improved the muscle strength ( p0.05 ) and fast walking speed ( p

In recent years, the development of myoelectric control for robotic lower-limb prostheses makes it possible for amputee users to volitionally control prosthetic joints. However, the human-centered control loop is not closed due to the lack of sufficient feedback of prosthetic joint movement, and it may result in poor control performance. In this research, we propose a vibrotactile stimulation system to provide the feedback of ankle joint position, and validate the necessity of combining it with volitional myoelectric control to achieve improved control performance. The stimulation system is wearable and consists of six vibrators. Three of the vibrators are placed on the anterior side of the thigh and the other three on the posterior side of the thigh. To explore the potential of applying the proposed vibrotactile feedback system for prosthetic ankle control, eight able-bodied subjects and two transtibial amputee subjects (TT1 and TT2) were recruited in this research, and several experiments were designed to investigate subjects' sensitivities to discrete and continuous vibration stimulations applied on the thigh. Then, we proposed a stimulation controller to produce different stimulation patterns according to current ankle angle. Amputee subjects were asked to control a virtual ankle displayed on the computer screen to reach different target ankle angles with a myoelectric controller, and control performances under different feedback conditions were compared. Experimental results indicated that subjects were more sensitive to stimulation position changes (identification accuracies were 96.39pm0.86%, 91.11% and 93.89% for able-bodied subjects, TT1 and TT2, respectively) than stimulation amplitude changes (identification accuracies were 89.89pm2.40%, 87.04% and 85.19% for able-bodied subjects, TT1 and TT2, respectively). Response times of able-bodied subjects, TT1 and TT2 to stimulation pattern changes were 0.47pm0.02s, 0.53s and 0.48s, respectively. Furthermore, for both TT1 and TT2, the absolute error of virtual ankle control reduced by about 50% with the addition of vibrotactile feedback. These results suggest that it is promising to apply the vibrotactile feedback system for the control of robotic transtibial prostheses.

In the past decades, researchers have deeply studied pathological and nonpathological gait to understand the human ankle function during walking. These efforts resulted in the development of new lower limb prosthetic devices aiming at raising the 3C-level (control, comfort, and cosmetics) of amputees. Thanks to the technological advances in engineering and mechatronics, challenges in the field of prosthetics have become an important source of interest for roboticists. Currently, most of the bionic feet are still on a research level but show promising results and a preview of tomorrow's commercial prosthetic devices. In this paper, the authors present the current state-of-the-art and the latest advances in propulsive bionic feet with its actuation principles. The context of this review study is outlined followed by a brief description of the basics in human biomechanics and criteria for new prosthetic designs. A new categorization based on the actuation principle of propulsive ankle-foot prostheses is proposed. Based on simulations, the general principles and benefits of each actuation method are explained. The corresponding latest advances in propulsive bionic feet are presented together with their main characteristics and scientific outcomes. The authors also propose to the reader a comparison analysis of the presented devices with a discussion of the general tendencies in new prosthetic feet.

Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs) and eight force sensors. The system was built to measure both kinematic (tilt angles) and dynamic (ground contact forces) signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA) classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.

Stable bipedal walking is one of the most important components of humanoid robot design, which can help us better understand natural human walking. In this paper, to study gait selection and gait transition of efficient bipedal walking, we proposed a dynamic bipedal walking model with an upper body, flat feet and compliant joints. The model can achieve stable cyclic motion with different walking gaits. The hip actuation and ankle stiffness behavior of the model are quite similar to those of human normal walking. In simulation, we studied the influence of hip actuation and ankle stiffness on walking performance of each gait. The effects of ankle stiffness on gait selection are also analyzed. Gait transition is realized by adjusting ankle stiffness during walking.